In addition to the above studies, there are several \(k\) NN based ensemble techniques proposed to improve the model accuracy and make the models more diverse. Breiman in [1]} suggested a bootstrap aggregation (or simply bagging) procedure, it is one of the basic ensemble methods and develops a novel model to estimate the exact bootstrap average in the long run of the model [2]}, [3]}, [4]}. Bagging is the fundamental block of many classical ensemble procedures. Methods based on the bagging technique draw a large number of random bootstrap samples of the same size and construct a base classifier on each sample. The final estimated class of a test point is the majority vote in the results given by all base learners [1]}. The exact bagging technique is further extended by Steel [6]} to sub-sampling with and without replacement aggregation. Several ensemble procedures have been proposed that use bagging and features subsets for constructing base \(k\) NN learners [7]}, [8]}, [9]}, [10]}. Moreover, [11]}, [12]} have proposed \(k\)  value optimization in base \(k\) NN classifiers to construct ensemble methods. The idea of boosting \(k\) NN is given by Pedrajas and Boyer [13]}, it is a combination of two methodologies to improve the performance of the ensemble. The first methodology is based on feature subset while the second is based on the transformation of the feature space using a non-linear projection. Other studies can be seen in [14]}, [15]}, [16]}, [17]}, [18]}, [19]} to boost up the \(k\) NN procedure and get improved results.
